This Is A Collaborative Learning Community Class Assignment

This Is A Collaborative Learning Community Clc Assignmentbefore Beg

This is a Collaborative Learning Community (CLC) assignment. Before beginning this assignment, each group should submit a filled-in copy of the CLC Agreement Form. Each CLC team will design a quasi or a true experimental study, investigating the impact of the independent variable on the dependent variable. Address the following in words: 1. Design either a quasi or experimental study to investigate the variables. What is the hypothesis? Describe the types of hypotheses with respect to testing. What does the experimental method allow that the correlation design does not? 2. Identify the independent variable. Identify the dependent variable. 3. Describe how the group will define operationally and measure the variables. 4. Describe how the group will obtain a random sample of participants. 5. Discuss how the group will ensure the study has high internal validity. Will the subjects be assigned randomly to the groups? Why or why not. 6. Are there any ethical concerns about the treatment of participants emerging from the experiment? 7. Consider the data presented, would you use t or F score? Why? include the appropriate effect size. 8. Submit an SPSS output for the quasi or true experimental study. Include at least two to four scholarly sources. While APA style is not required for the body of this assignment, solid academic writing is expected and in-text citations and references should be presented using APA documentation guidelines, which can be found in the APA Style Guide, located in the Student Success Center. This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.

Paper For Above instruction

The proposed study aims to investigate the impact of a specific independent variable on a dependent variable through a quasi-experimental design. This approach is selected due to practical considerations such as participant availability or ethical constraints that prevent full random assignment. The core hypothesis posits that manipulating the independent variable will produce a significant effect on the dependent variable, aligning with the fundamental premise of experimental research. Unlike correlation studies that merely identify relationships without causal inference, experimental methods permit the examination of causal effects by controlling extraneous variables and establishing temporal precedence.

In this study, the independent variable will be clearly defined operationally and measured by specific, observable criteria. For instance, if the independent variable is a teaching intervention, operationalization may involve the implementation of a particular instructional technique, with measurement based on adherence to protocol or student engagement levels. Conversely, the dependent variable, such as academic achievement, will be operationally defined through standardized test scores or grade point averages, ensuring reliability and validity in measurement.

Sampling will be conducted through a randomized procedure to enhance representativeness and reduce selection bias. Students from an accessible population, such as a specific class or school district, will be randomly selected using random number generators or lottery methods. Random sampling is critical to generalize findings and bolster external validity.

To ensure high internal validity, the study will employ careful control of extraneous variables, such as environmental factors or participant characteristics, and utilize techniques like matching or statistical controls if random assignment is not feasible. When possible, subjects will be randomly assigned to experimental and control groups to strengthen causal inferences. If random assignment is impractical, a comparison group that naturally differs in certain aspects may be used, with appropriate statistical adjustments.

Ethical considerations are paramount; the study will adhere to institutional review board (IRB) guidelines, ensuring informed consent, confidentiality, and the right to withdraw without penalty. Any potential harm or discomfort must be minimized, and debriefing will be provided post-study.

Data analysis will depend on the nature and number of groups and variables. If comparing two groups, a t-test may be appropriate to assess mean differences. For multiple group comparisons or factorial designs, an F-test (ANOVA) would be suitable. Effect size measures, such as Cohen’s d or eta-squared, will accompany the statistical tests to interpret the practical significance of findings.

Finally, an SPSS output will be generated following data analysis, illustrating the statistical test result, significance levels, and effect size. This empirical evidence will support conclusions drawn from the study.

References

• Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
• Field, A. (2013). Discovering statistics using IBM SPSS statistics. Sage.
• Gravetter, F. J., & Forzano, L. B. (2018). Research methods for the behavioral sciences (6th ed.). Cengage Learning.
• Keppel, G., & Wickens, T. D. (2004). Design and analysis: A researcher's handbook. Pearson.
• Robson, C. (2011). Real world research (3rd ed.). Wiley.
• Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Pearson.
• Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin.
• Morling, B. (2017). Research methods in psychology (3rd ed.). W.W. Norton & Company.
• Polit, D. F., & Beck, C. T. (2012). Nursing research: Generating and assessing evidence for nursing practice. Lippincott Williams & Wilkins.
• Maxwell, S. E., & Schmidt, J. A. (2004). Validity and validation in social, behavioral, and health sciences. SAGE Publications.